Translation of artificial intelligence representations

ABSTRACT

Techniques for translating graphical representations of domain knowledge are provided. In one example, a computer-implemented method comprises receiving, by a device operatively coupled to a processor, a graphical representation of domain knowledge. The graphical representation comprises information indicative of a central concept and at least one chain of events associated with the central concept. The computer-implemented method further comprises translating, by the device, the graphical representation into an artificial intelligence planning problem. The artificial intelligence planning problem is expressed in an artificial intelligence description language. The translating comprises parsing the graphical representation into groupings of terms. A first grouping of terms of the grouping of terms comprises an event from the at least one chain of events and a second grouping of terms of the grouping of terms comprises the information indicative of the central concept. The computer-implemented method also comprises validating, by the device, the artificial intelligence planning problem.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Contract No.:H98230-13-D-0054/0006 awarded by Department of Defense. The Governmenthas certain rights in this invention.

BACKGROUND

The subject disclosure relates to artificial intelligencerepresentations, and more specifically, to translation of a graphicalrepresentation of domain knowledge associated with an artificialintelligence planning problem.

Automated planning and scheduling is a branch of artificial intelligence(AI) that concerns the realization of strategies or action sequences,typically for execution by intelligent agents, autonomous robots, andunmanned vehicles. Unlike classical control and classification problems,solutions are complex and must be discovered and optimized inmultidimensional space. Planning is also related to decision theory.Planning may be performed such that solutions may be found and evaluatedprior to execution; however, any derived solution often needs to berevised. Solutions usually resort to iterative trial and error processescommonly seen in artificial intelligence. These include dynamicprogramming, reinforcement learning, and combinatorial optimization.

A planning problem generally comprises the following main elements: afinite set of facts, the initial state (a set of facts that are trueinitially), a finite set of action operators (with precondition andeffects), and a goal condition. An action operator maps a state intoanother state. In the classical planning, the objective is to find asequence of action operators (or planning action) that, when applied tothe initial state, will produce a state that satisfies the goalcondition. This sequence of action operators is called a plan.

Plan recognition is the problem of recognizing the plans and the goalsof an agent given a set of observations. There exist a number ofdifferent approaches to the plan recognition problem including the useof SAT solvers and planning where the domain theory is given as an inputas well as the use of techniques that assume a plan library is given asan input. Plan recognition continues to be an important problem to studyas it has many practical applications such as assisted cognition,computer games, and network monitoring.

For example, as described in Gregor Behnke et al., “IntegratingOntologies and Planning for Cognitive Systems” (Proc. Of the 28^(th)Int. Workshop on Description Logics (2015)), “patterns and mechanismsthat suitably link planning domains and interrelated knowledge in anontology” are described. In Gregor Behnke et al., “an approach for usingan ontology as the central source of domain knowledge for a cognitivesystem” is described. However, Gregor Behnke et al. does not presentsolutions to encoding domain knowledge that do not require specializedknowledge of the ontology.

As another example, as described in Rabia Jilani, “ASCoL: A Tool forImproving Automatic Planning Domain Model Acquisition” (Advances inArtificial Intelligence, 2015), “AI planning requires domain models.”Rabia Jilani introduces “ASCoL, a tool that exploits graph analysis forautomatically identifying static relations, in order to enhance planningdomain models.” However, Rabia Jilani does not address receipt ofdynamic information, or lack of plan traces in the received information.Furthermore, Rabia Jilani does not present solutions to encoding domainknowledge that do not require specialized knowledge of the plan traces.

Furthermore, many other conventional approaches to plan recognition alsorequire specialized knowledge of artificial intelligence descriptionlanguages in order to be useful. It may be relatively difficult for aperson having specialized domain knowledge, but lacking specializedartificial intelligence training, to fully utilize artificialintelligence planning. Furthermore, a lack of accurately encoded domainknowledge in the artificial intelligence description language canprohibit the use of artificial intelligence planning for planrecognition problems in general.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusand/or computer program products that facilitate machine learning toprovide incremental static program analysis.

Mind maps are examples of diagrams or graphical representations thatrepresent domain knowledge. Mind maps can represent domain concepts, andrelationships to other concepts, in a simple form. While it is oftenchallenging to encode this domain knowledge in a planning language orartificial intelligence description language, it can be easier toexpress the domain knowledge graphically in mind maps. For example, andaccording to one or more embodiments of this disclosure, a domain experthaving little or no knowledge of a particular artificial intelligencedescription language can still accurately express domain knowledge in agraphical representation. Furthermore, according to one or moreembodiments of this disclosure, the graphical representation can beautomatically translated into any particular artificial intelligencedescription language. Accordingly, one or more embodiments of thisdisclosure provide artificial intelligence planning solutions wheredomain experts are not required to have specialized artificialintelligence training.

According to an embodiment, a computer-implemented method is provided.The computer-implemented method can comprise receiving, by a deviceoperatively coupled to a processor, a graphical representation of domainknowledge. The graphical representation can comprise informationindicative of a central concept and at least one chain of eventsassociated with the central concept. The computer-implemented method canfurther comprise translating, by the device, the graphicalrepresentation into an artificial intelligence planning problem. Theartificial intelligence planning problem can be expressed in anartificial intelligence description language. The translating cancomprise parsing the graphical representation into groupings of terms. Afirst grouping of terms of the grouping of terms can comprise an eventfrom the at least one chain of events and a second grouping of terms ofthe grouping of terms can comprise the information indicative of thecentral concept. The translating can also comprise identifying aninitial state of the domain knowledge based on the first grouping ofterms and the second grouping of terms. The computer-implemented methodcan also comprise validating, by the device, the artificial intelligenceplanning problem. According to this example, benefits are realized inthat a graphical representation of domain knowledge is automaticallyencoded into the artificial intelligence description language. Furtherbenefits include the validating to ensure the artificial intelligenceplanning problem is a valid artificial intelligence planning problem.

According to another embodiment, the computer-implemented method cancomprise identifying one or more constants based on the first groupingof terms, identifying one or more predicates and one or more actionsbased on the one or more constants, and encoding the initial state, theone or more constants, the one or more predicates, and the one or moreactions into the artificial intelligence description language.Therefore, according to this example, several aspects of specializedknowledge in artificial intelligence planning are handled in novelmanner. For example, constants, predicates, and actions can beautomatically encoded through the facilitation of the described example.

According to another embodiment, the graphical representation cancomprise one or more weights associated with an event of the at leastone chain of events, where the translating can further compriseassociating the one or more weights with the one or more predicates andthe one or more actions. Therefore, according to this example,relatively complex graphical representations comprising additionalfeatures, such as weighted edges, can also be automatically encode inthe artificial intelligence description language. Accordingly, while adomain expert may have specialized domain knowledge related to theweights and weighted edges, no specialized artificial intelligenceplanning knowledge is required.

According to another embodiment, domain knowledge of the graphicalrepresentation of domain knowledge can be changed, and thecomputer-implemented method can further comprise propagating the changedgraphical representation of domain knowledge. Therefore, according tothis example, on-the-fly changes to domain knowledge can be handledwithout requiring specialized knowledge of the artificial intelligencedescription language.

According to another embodiment, the computer-implemented method canalso comprise receiving, by the electronic device, a second graphicalrepresentation, and updating, by the electronic device, the initialstate of the domain knowledge based on the second graphicalrepresentation. Therefore, according to this example, multiple newgraphical representations of domain knowledge can be processed.Accordingly, new domain knowledge, current domain knowledge, orotherwise altered domain knowledge can be taken into considerationwithout a domain expert having any specialized artificial intelligencetraining.

According to yet another embodiment, the validating of thecomputer-implemented method can comprise determining, by the device,through an artificial intelligence planning component an artificialintelligence plan as a solution to the artificial intelligence planningproblem, and determining, by the device, that the artificialintelligence plan include a valid pathway of the graphicalrepresentation of the artificial intelligence planning problem.Therefore, according to this example, the computer-implemented methodcan ensure the artificial intelligence planning problem is a validartificial intelligence planning problem as related to the graphicalrepresentation provided by the domain expert.

In some embodiments, elements described in connection with thecomputer-implemented can be embodied in different forms such as one ormore program instructions stored on a computer readable storage medium,a computer program product, a system, or another form.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a diagram of an example, non-limiting graphicalrepresentation of domain knowledge associated with an artificialintelligence planning problem in accordance with one or more embodimentsof the disclosed subject matter.

FIG. 2 illustrates a block diagram of an example, non-limiting systemthat can translate graphical representations of domain knowledgeassociated with an artificial intelligence planning problem inaccordance with one or more embodiments of the disclosed subject matter.

FIG. 3 illustrates a flowchart of an example, non-limitingcomputer-implemented method of translating graphical representations ofdomain knowledge associated with an artificial intelligence planningproblem in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 4 illustrates a flowchart of an example, non-limitingcomputer-implemented method of translating graphical representations ofdomain knowledge associated with an artificial intelligence planningproblem in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 5 illustrates a flowchart of an example, non-limitingcomputer-implemented method of translating graphical representations ofdomain knowledge associated with an artificial intelligence planningproblem in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 6 illustrates a block diagram of a non-limiting example of a parsedgraphical representation of domain knowledge associated with anartificial intelligence planning problem in accordance with certainembodiments of the disclosed subject matter.

FIG. 7 illustrates a block diagram of a non-limiting example of anartificial intelligence planning problem in accordance with certainembodiments of the disclosed subject matter.

FIG. 8 illustrates a block diagram of a non-limiting example of avalidated artificial intelligence planning problem in accordance withcertain embodiments of the disclosed subject matter.

FIG. 9 illustrates a block diagram of a non-limiting example of asolution of a problem model in accordance with certain embodiments ofthe disclosed subject matter.

FIG. 10 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

This disclosure relates in some embodiments to artificial intelligencerepresentations, and more specifically, to translation of a graphicalrepresentation of domain knowledge associated with an artificialintelligence planning problem.

Mind maps are examples of diagrams or graphical representations that canrepresent domain knowledge. These graphical representations canrepresent domain concepts, and relationships to other concepts, in asimple form. While it is often challenging to encode this domainknowledge in a planning language or artificial intelligence descriptionlanguage, it can be easier to express the domain knowledge graphicallyin a diagram. For example, and according to one or more embodiments ofthis disclosure, a domain expert having little or no knowledge of aparticular artificial intelligence description language can stillaccurately express domain knowledge in a graphical representation.Furthermore, according to one or more embodiments of this disclosure,the graphical representation can be automatically translated into anyparticular artificial intelligence description language. Accordingly,embodiments of this disclosure provide artificial intelligence planningsolutions where domain experts are not required to have specializedartificial intelligence training.

In this disclosure, several computer-implemented methods are describedthat facilitate extraction of artificial intelligence planning knowledgefrom graphical representations in an automated manner. This facilitatesthe use of artificial intelligence planning for many applicationsincluding plan recognition, diagnosis, and explanation generation.Accordingly, in situations where observations need to be explained givendomain knowledge, one or more embodiments of this disclosure provide forautomatically extracting this domain knowledge without having the domainknowledge formally represented and encoded in an artificial intelligencedescription language.

According to one or more embodiments, computer-implemented methods areprovided that can receive one or more graphical representations thatrepresent the domain knowledge, and that can automatically translate thegraphical representations into an artificial intelligence planningproblem. The generated artificial intelligence planning problem can bethen transmitted to an artificial intelligence planning component togenerate one or more plans. The graphical representations can bereceived from any suitable source, and can be presented in any suitablemanner.

Several benefits of some embodiments of this disclosure can includecapture of the concepts and relationships provided graphically in asimple and easy to use approach. This disclosure can also facilitatemore effective and efficient execution of artificial intelligenceplanning via automation from concepts described in graphicalrepresentations to an end result of an accurate artificial intelligenceplanning problem. It follows then, that further benefits of someembodiments of this disclosure can include validation of the artificialintelligence planning problem and generation of solutions to theartificial intelligence planning problem.

According to one or more embodiments disclosed herein, the translationof graphical representations of domain knowledge into an artificialintelligence description language is presented using a novel approach.According to at least one embodiment, a validation technique is alsodescribed for validation of outputs. To this end, a multi-steptranslation technique is described in detail that facilitates the use ofartificial intelligence planning for the computation of the posteriorprobabilities of the possible goals, thereby validating the translation.

Turning now to the drawings, FIG. 1 illustrates a diagram of an example,non-limiting graphical representation of domain knowledge 100 associatedwith an artificial intelligence planning problem in accordance with oneor more embodiments of the disclosed subject matter. The graphicalrepresentation of domain knowledge 100 can comprise a central concept102, leading indicator 104, leading indicator 106, leading indicator108, implication 110, implication 112, and implication 114. Thegraphical representation of domain knowledge 100 can further compriseimplication 116 associated with implication 116, weight 122 associatedwith leading indicator 104, and weight 124 associated with implication114.

It should be understood that the particular arrangement of the graphicalrepresentation of domain knowledge 100 can change according toassociated domain knowledge. For example, more or fewer leadingindicators, weights, and/or implications can be included according toany particular implementation and associated domain knowledge.Additionally, more or fewer weights associated with any number ofleading indicators and/or implications can also be applicable.Accordingly, the particular form of the graphical representation ofdomain knowledge 100 should not be considered limiting in any manner.

As shown in FIG. 1, the central concept 102 is a general conceptrepresentative of a desirable occurrence or force in an associateddomain. For example, a possible force can include a “low oil price”.Other possible forces can also be applicable to particular domains.Accordingly, exhaustive description of a plurality of different forcesand central concepts is omitted herein for the sake of brevity.

As further shown in FIG. 1, there are several arrows providing fordirectionality to and from the central concept 102. From the arrows,several traits of the domain knowledge can be inferred. For example,arrows directed towards the central concept 102 can be indicative of asource concept being a “leading indicator”. Furthermore, arrows directedfrom the central concept 102 can be indicative of a possible“implication”. Moreover, several concepts can be tied together toestablish a chain of events leading to or from the central concept inany desirable manner Thus, the particular arrangement of arrowsillustrated in FIG. 1 are merely examples of one possible graphicalrepresentation of a particular domain knowledge, and therefore shouldnot be construed as limiting.

As further shown in FIG. 1, the central concept 102 can be associatedwith leading indicator 104, leading indicator 106, and leading indicator108. The leading indicators can represent a portion of the domainknowledge of the graphical representation of domain knowledge 100. Theleading indicators can comprise concepts that can affect the centralconcept 102. For example, and without limitation, continuing the exampleof a central concept of a “low oil price,” a possible leading indicatorcan include a “warmer than normal winter in the northern hemisphere.”Thus, if this leading indicator were true, a resulting implication mayresult that affects the central concept.

As further shown in FIG. 1, the central concept 102 may be associatedwith implication 110, implication 112, and implication 114.Additionally, implication 116 can be associated with implication 116.The implications may represent a portion of the domain knowledge of thegraphical representation of domain knowledge 100. The implications caninclude concepts or implications affected by the leading indicators andany possible occurrence. For example, and without limitation, continuingthe example of a central concept of a “low oil price,” a possibleimplication can include “oil prices continue to rise.” Thus, if theexample leading indicator of a “warmer than normal winter in thenorthern hemisphere” were true, the possible implication of “oil pricescontinue to rise” may also be true as an implication of the leadingindicator and central concept. Furthermore, as illustrated in FIG. 1,under some circumstances, implication 116 may result as a link in achain of events beginning with a particular leading indicator beingtrue, that particular leading indicator causing implication 114, andimplication 114 therefore causing implication 116. Other chains ofevents can also be applicable under some circumstances.

As further shown in FIG. 1, leading indicator 104 can include a weight122 associated the leading indicator 104. Additionally, the implication114 can include a weight 124 associated with the implication 114.Generally, the weight 122 and the weight 124 can represent an actioncost or other similar attribute. The action cost can indicate a cost ofperforming an action associated with the leading indicator 104 and/orthe implication 114. Furthermore, although not illustrated for clarity,according to at least one embodiment, every leading indicator caninclude an associated weight or cost. Additionally, according to atleast one embodiment, every implication can include an associated weightor cost. Similarly, according to some embodiments, at least one or moreleading indicators does not have an associated weight or cost. Moreover,according to some embodiments, at least one or more implications doesnot have an associated weight or cost. Thus, the particular arrangementof the graphical representation of domain knowledge 100 can be varied inmany ways.

According to one or more embodiments, the graphical representation ofdomain knowledge 100 can be parsed and translated into an artificialintelligence planning problem as an initial output. The artificialintelligence planning problem can be expressed in an artificialintelligence description language. For example, and without limitation,an artificial intelligence description language can include a form ofPlanning Domain Description Language. However, it is to be understoodthan any suitable artificial intelligence description language can beapplicable. Upon translation into the artificial intelligence planningproblem, one or more embodiments can also include validating theartificial intelligence planning problem to create a validatedartificial intelligence planning problem as an additional output. Eitherof the artificial intelligence planning problem and the validatedartificial intelligence planning problem can be beneficial in tuningoperation of different translation components that are described morefully below.

FIG. 2 illustrates a block diagram of an example, non-limiting system200 that can translate graphical representations of domain knowledgeassociated with an artificial intelligence planning problem inaccordance with one or more embodiments of the disclosed subject matter.The system 200 can include a translation component 204 and a validationcomponent 210 electrically coupled to the translation component 204.Furthermore, the translation component can include a parsing component206 and one or more processing units 207 arranged therein. Additionally,the validation component 210 can include an artificial intelligenceplanning component 212 and one or more processing units 213 arrangedtherein. Other arrangements of the individual illustrated components ofthe system 200 can also be applicable. Therefore, the particulararrangement illustrated is one non-limiting example. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

As shown in FIG. 2, the translation component 204 can receive input viaan electronic input device and can output an artificial intelligenceplan via an electronic output device. Accordingly, the translationcomponent 204 can perform one or more functions of the system 200.Furthermore, the validation component 210 can receive input via anelectronic input device and can output a validated artificialintelligence plan via an electronic output device. Accordingly, thevalidation component 210 can perform one or more functions of the system200.

The translation component 204, including the parsing component 206, isan artificial intelligence component that can be arranged to perform acomputer-implemented method. Accordingly, the translation component 204can include defined hardware (e.g., the one or more processing units207) configured to automate several program instructions describedherein. The translation component 204 can also be implemented as adiscrete set of program instructions that implement any portion of thedescribed methodologies in any ordered sequence. Furthermore, althoughillustrated as a distinct component, the parsing component 206 can alsobe integrated within the translation component 204. As illustrated, thetranslation component 204 can receive as input the graphicalrepresentation of domain knowledge 100 for processing.

Responsive to receiving the graphical representation of domain knowledge100, the translation component 204 can translate the graphicalrepresentation of domain knowledge 100 into an artificial intelligenceplanning problem 208. The translation of the translation component 204can be facilitated using a translation algorithm, which is describedmore fully below. The translation component 204 can process andtranslate the graphical representation of domain knowledge 100, and canoutput the artificial intelligence planning problem 208.

The artificial intelligence planning problem 208 can include a pluralityof data and information relating to the graphical representation ofdomain knowledge 100. For example, and as illustrated in FIG. 7, theartificial intelligence planning problem 208 can include temporalinformation 702, an initial state 704, and a set of goals 706.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity.

Generally, an artificial intelligence planning problem can berepresented based on Definition 1 and the plan recognition can be basedon Definition 2, shown below.

Definition 1: A planning problem with action costs is a tuple P=(F, A,I, G), where F is a finite set of fluent symbols, A is a set of actionswith preconditions, Pre(a), add effects, Add(a), delete effects, Del(a),and non-negative action costs, Cost(a), I⊆F defines the initial state,and G⊆F defines the goal state.

A state, s, is a set of fluents with known truth value. An action a isexecutable in a state s if Pre(a)⊆s. The successor state is defined asδ(a; s)=((s\ Del(a))∪Add(a)) for the executable actions. The sequence ofactions π=[a₁, . . . , a_(n)] is executable in s if the states′=δ(a_(n), δ(a_(n-1), . . . , δ(a⁻¹, s))) is defined. Moreover, π isthe solution to the planning problem P if it is executable from theinitial state and G⊆δ(a_(n), δ(a_(n-1), . . . , δ(a⁻¹,I))). Furthermore,π is said to be optimal if it has minimal cost, or there exists no otherplan that has a better cost than this plan. A planning problem P mayhave more than one optimal plan. Also note that the tuple (F, A, I) isoften referred to as the planning domain.

As relayed in Definition 1, the term “fluents” can refer to fluentconditions and/or other conditions that change over time. Fluentconditions can be conditions that change over time, and can include avariety of conditions associated with the domain knowledge representedby the graphical representation of domain knowledge 100. These fluentconditions can include, for example, degradation of an object, trafficwithin a loading dock, weather conditions over a defined geography, andother suitable conditions. Other fluent conditions and/or conditionsthat change over time are also contemplated, and the examples providedherein are not to be construed as limiting in any manner.

Furthermore, an artificial intelligence plan recognition problem forprocessing by an artificial intelligence planning component can berepresented as set forth in Definition 2, below.

Definition 2: An artificial intelligence plan recognition problem is atuple PR=(F, A, I, O, Γ, PROB), where (F, A, I) is the planning domainas defined in Definition 1 above, O=[o₁, . . . , o_(m)], where o_(i)∈F,i∈[1, m] is the sequence of observations, Γ is the set of possible goalsG, G⊆F, and PROB is the goal priors, P (G).

Unreliable, unexplainable, or otherwise noisy observations are definedas those that have not been added to the state as a result of an effectof any of the actions in a plan for a particular goal, while missingobservations are those that are added to the state but are not observed(i.e., are not part of the observation sequence). To address the noisyobservations, the definition of satisfaction of an observation sequenceby an action sequence is modified to allow for observations to be leftunexplained. Given an execution trace and an action sequence, anobservation sequence is said to be satisfied by an action sequence andits execution trace if there is a non-decreasing function that maps theobservation indices into the state indices as either explained ordiscarded.

Turning back to FIG. 2, the validation component 210, including theartificial intelligence planning component 212, is an artificialintelligence validation component that can be arranged to perform acomputer-implemented method. Accordingly, the validation component 210can include defined hardware (e.g., the one or more processing units213) configured to automate several program instructions describedherein. The validation component 210 can also be implemented as adiscrete set of program instructions that implement the describedmethodologies in any ordered sequence. Furthermore, although illustratedas a distinct component, the artificial intelligence planning component212 can also be integrated within the validation component 210. Asillustrated, the validation component 210 can receive as input thegraphical representation of domain knowledge 100 and the artificialintelligence planning problem 208 for processing.

Responsive to receiving the graphical representation of domain knowledge100 and the artificial intelligence planning problem 208, the validationcomponent 210 can validate the artificial intelligence planning problem208. The validation of the validation component 210 can be facilitatedusing a validation algorithm, which is described more fully below.Generally, validation can include, for example, determining a solutionto the artificial intelligence planning problem 208 via the artificialintelligence planning component 212. Upon determining the solution, thevalidation component 210 can determine if a valid path, according to thedetermined solution, exists within the graphical representation ofdomain knowledge 100. Such a valid path validates the solution, andtherefore validates the artificial intelligence planning problem 208.Accordingly, the validation component 210 can process the graphicalrepresentation of domain knowledge 100 and the artificial intelligenceplanning problem 208, and can output a validated artificial intelligenceplanning problem 214.

The validated artificial intelligence planning problem 214 can comprisea plurality of valid data and valid information relating to thegraphical representation of domain knowledge 100. For example, and asillustrated in FIG. 8, the validated artificial intelligence planningproblem 214 can comprise valid temporal information 802, a valid initialstate 804, and a valid set of goals 806. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity.

Responsive to validating the artificial intelligence planning problem208, the validation component 210 can provide validation feedback 220 tothe translation component 204. For example, as illustrated in FIG. 9,validation feedback 220 can include the validated solution of theartificial intelligence planning problem and/or a feedback as to theartificial intelligence planning problem 208 being invalid. A validatedsolution can include a posterior probability of goals given observations902. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

The solution to the artificial intelligence planning recognition problemcan comprise the posterior probabilities, the probability of a plangiven the observations, P (π|O), and/or the probability of a goal giventhe observations, P (G|O). P (π|O) can be approximated by normalizingthree objectives over a set of sample plans: (i) cost of the originalactions, (ii) number of missing observations, and (iii) number of noisyobservations. P (G|O) can be computed by a summation over P (π|O) forall plans that achieve G and satisfy O. The sampled set of plans can becomputed using either diverse planning or top-k planning on thetransformed planning problem. Additionally, the sampled set of plans canbe computed using other artificial intelligence planning solutions.Accordingly, the artificial intelligence planning problem projects afuture state of the domain knowledge.

As described above, the system 200 can include a translation component204 configured to translated the graphical representation of domainknowledge 100 and output an artificial intelligence planning problem208. Furthermore, the validation component 210 can be configured tovalidate the artificial intelligence planning problem 208 and output avalidated artificial intelligence planning problem 214. Hereinafter, amore detailed discussion of one or more embodiments comprisingcomputer-implemented methods for facilitating translating of thegraphical representation of domain knowledge 100 is provided.

FIG. 3 illustrates a flowchart of an example, non-limitingcomputer-implemented method 300 of translating graphical representationsof domain knowledge associated with an artificial intelligence planningproblem in accordance with one or more embodiments of the disclosedsubject matter.

The computer-implemented method 300 can include, for example, a sequenceof program instructions to be performed by a translation component, suchas the translation component 204. The computer-implemented method 300can include, but is not limited to, block 302, block 304, and block 306.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for the sake of brevity.

The computer-implemented method 300 can comprise receiving, by a deviceoperatively coupled to a processor, a graphical representation of domainknowledge, at block 302 (e.g., by translation component 204). Thegraphical representation of domain knowledge can be received from anelectronic input device.

According to at least one embodiment, the graphical representation cancomprise information indicative of a central concept and at least onechain of events associated with the central concept. Generally, theinformation indicative of a central concept can be encoded into thegraphical representation as a centralized concept with one or morearrows flowing to and/or from the central concept.

The computer-implemented method 300 can also comprise translating, bythe device, the graphical representation into an artificial intelligenceplanning problem, at block 304 (e.g., by translation component 204). Theartificial intelligence planning problem can be expressed in anartificial intelligence description language. As a non-limiting example,the artificial intelligence description language can include a planningdomain description language (PDDL), a planning domain definitionlanguage, an artificial intelligence planning language, or any othersuitable artificial intelligence language. Suitable artificialintelligence description languages may include languages where encodingof domain information is supported.

Generally, the translating of block 304 can include additionalfunctional features for parsing and assembling the graphicalrepresentation into the artificial intelligence planning problem. Forexample, further details regarding at least one embodiment for parsingand assembling the graphical representation into the artificialintelligence planning problem are described more fully below.Accordingly, when processed by the translation component 204, thetranslation of the graphical representation of domain knowledge 100 intothe artificial intelligence planning problem 208 cannot be performed bya human (e.g., is greater than the capability of a single human mind).For example, an amount of data processed, a speed of processing of dataand/or data types processed by the translation component over a certainperiod of time can be greater, faster and different than an amount,speed and data type that can be processed by a single human mind overthe same period of time.

Responsive to block 304, the computer-implemented method 300 can alsocomprise validating, by the device, the artificial intelligence planningproblem, at block 306 (e.g., by validation component 210). Generally,the validating can be facilitated by a validation algorithm facilitatedby the artificial intelligence planning component 212. Other forms ofvalidation may also be applicable, and are described more fully below.Accordingly, when processed by the validation component 210, thegeneration of a solution to the artificial intelligence planning problem208 to create a validated artificial intelligence planning problem 214(e.g., is greater than the capability of a single human mind). Forexample, an amount of data processed, a speed of processing of dataand/or data types processed by the validation component over a certainperiod of time can be greater, faster and different than an amount,speed and data type that can be processed by a single human mind overthe same period of time.

Responsive to the validating, the validation component 210 can output avalidated artificial intelligence planning problem as described indetail above.

As described with reference to FIG. 3, a computer-implemented method oftranslating a graphical representation of domain knowledge into anartificial intelligence planning problem can comprise translatingthrough use of a translation algorithm. Details related to oneembodiment of a translation algorithm are presented with reference toFIG. 4.

FIG. 4 illustrates a flowchart of an example, non-limitingcomputer-implemented method 400 of translating graphical representationsof domain knowledge associated with an artificial intelligence planningproblem in accordance with one or more embodiments of the disclosedsubject matter.

The computer-implemented method 400 can comprise, for example, asequence of program instructions to be performed by a parsing componentand/or a translation component, such as the parsing component 206 andthe translation component 204. The computer-implemented method 400 caninclude, but is not limited to, block 402 and block 404. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The computer-implemented method 400 can comprise, for example, parsingthe graphical representation into groupings of terms, at block 402(e.g., by parsing component 206). According to block 402, a firstgrouping of terms of the grouping of terms can comprises an event fromthe at least one chain of events. Furthermore, a second grouping ofterms of the grouping of terms can comprise the information indicativeof the central concept. For example, as illustrated in FIG. 6, theparsed graphical representation of domain knowledge can be organizedinto the groupings of terms. The groupings of terms can include one ormore chains of events associated with leading indicators, one or morechains of events associated with implications, one or more action costsand/or weights, and information indicative of a central concept.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity.

According to at least one embodiment, the computer-implemented method400 can also comprise identifying one or more constants based on thefirst grouping of terms (e.g., by the translation component 204). Forexample and without limitation, the one or more constants can comprise aset of constants indicating a set of occurrences and factors. The one ormore constants can be parsed from the graphical representation of domainknowledge and organized according to rules or procedures for theartificial intelligence description language. One possible organizationof example constants is presented below in Table 1.

TABLE 1 (:constants more-profits-for-local-big-businessesreduced-ability-to-invest-and-expand-operations tighter-monetary-policyvolatility-in-the-price-of-oil - occurrence decreased-revenue -bisimplication currency-appreciation social-stability - factor

It is noted that the examples presented in Table 1 are expressed in aparticular form of artificial intelligence description language, aPlanning Domain Description Language. Other forms of artificialintelligence description languages can also be applicable, and thereforethe particular form of Table 1 should not be construed as limiting. Itis further noted that particular example constants expressed in Table 1are associated with a single example, and should not be construed aslimiting.

According to at least one embodiment, the computer-implemented method400 can also comprise identifying one or more predicates and one or moreactions based on the one or more constants (e.g., by the translationcomponent 204). For example, the one or more predicates can be based onany leading indicators identifiable in the graphical representation ofdomain knowledge. Furthermore, the one or more actions can be based onedges or arrows identifiable in the graphical representation of domainknowledge, as described in detail above. Therefore, in an embodimentcomprising identifying one or more predicates and one or more actions,several aspects of specialized knowledge in artificial intelligenceplanning are handled in novel manner. For example, constants,predicates, and actions can be automatically encoded without requiringspecialized knowledge of the artificial intelligence descriptionlanguage.

The one or more predicates can be organized according to rules orprocedures for the artificial intelligence description language. Onepossible organization of example predicates is presented below in Table2.

TABLE 2 (:predicates   (_indicator ?y - factor ?x - occurrence)   (_next?x - occurrence ?x - occurrence)   (_next-bis ?x - occurrence ?x -busimplication)   (_fact ?x - occurrence)   (occur ?x - occurrence) )

It is noted that the examples presented in Table 2 are expressed in aparticular form of artificial intelligence description language, aPlanning Domain Description Language. Other forms of artificialintelligence description languages can also be applicable, and thereforethe particular form of Table 2 should not be construed as limiting.Following the example of Table 2, and somewhat similarly, the one ormore actions can be organized according to rule or procedures for theartificial intelligence description language. One possible organizationof example actions is presented below in Table 3.

TABLE 3 (:action next-state-transition :parameters (?x1 - occurrence?x2 - occurrence) :cost (10) :precondition (and (occur ?x1)):precondition (and (_next ?x1 ?x2)) :effect (and   (not (occur ?x1))  (occur ?x2)   (_fact ?x2) )) (:actionnext-state-transition-bis-implication :parameters (?x1 - occurrence?x2 - bisimplication) :cost (10) :precondition (and (occur ?x1)):precondition (and (_next-bis ?x1 ?x2)) :effect (and (not (occur ?x1))(_bis_ ?x2) )) (:action indicator-occurred :parameters (?y - factor ?x -occurrence) :cost (10) :precondition (and (_is-true ?y)) :precondition(and (_indicator ?y ?x)) :effect (and (not (_is-true ?y))   (occur ?x)  (_fact ?x)   ))

It is noted that the examples presented in Table 3 are expressed in aparticular form of artificial intelligence description language, aPlanning Domain Description Language. Other forms of artificialintelligence description languages can also be applicable, and thereforethe particular form of Table 3 should not be construed as limiting. Itis further noted that as presented with reference to Table 2 and Table3, the indicator “occurred” could be modified to assign weights and/orcosts to the different main concepts. The weights and/or costs can besimilar to the weights and/or costs described in detail above. Forexample, the weights and/or costs can also comprise a level ofimportance as well as a particular value. According to one embodiment,the level of importance can include “low,” “medium,” and/or “high”.Furthermore, each particular level of importance can be associated witha difference and/or unique cost representative of an edge weight. Otherforms of weights and/or costs can also be applicable. Therefore, theseparticular examples should not be construed as limiting.

Accordingly, the graphical representation can further comprise one ormore weights associated with an event of the at least one chain ofevents. Similarly, the graphical representation can also comprise one ormore probabilities between an event of the at least one chain of events.The probabilities can be represented as a different form of a weightand/or cost. Thus, the computer-implemented method 400 can furthercomprise associating the one or more weights with the one or morepredicates and the one or more actions as described above. Therefore, anin embodiment where the graphical representation comprises one or moreweights, relatively complex graphical representations can also beautomatically encoded in the artificial intelligence descriptionlanguage. Accordingly, while a domain expert may have specialized domainknowledge related to the weights and weighted edges, no specializedartificial intelligence planning knowledge is required.

According to at least one embodiment, the computer-implemented method400 can also comprise encoding the initial state, the one or moreconstants, the one or more predicates, and the one or more actions intothe artificial intelligence description language (e.g., by thetranslation component 204). For example, the encoding can compriseparsing and organizing each of the initial state, the one or moreconstants, the one or more predicates, and the one or more actions in asimilar manner as outlined in Table 1, Table 2, and Table 3.

The computer-implemented method 400 can further comprise identifying theinitial state of the domain knowledge based on the first grouping ofterms and the second grouping of terms, at block 404 (e.g., by parsingcomponent 206). Generally, the initial state can be identified throughthe graphical representation of domain knowledge by parsing andidentifying any leading indicators or other factors pointing to aninitial state of the domain knowledge. For example, one possibleindication of an initial state can comprise identifying whether aparticular graphical representation of domain knowledge can actuallyoccur by establishing a special predicate. One special predicate caninclude a “_is-true” predicate as set forth below in Table 4.

TABLE 4 (:init ;; initial state (_is-true social-unrest) (_is-truesocial-stability) )

It is noted that the examples presented in Table 4 are expressed in aparticular form of artificial intelligence description language, aPlanning Domain Description Language. Other forms of artificialintelligence description languages can also be applicable, and thereforethe particular form of Table 4 should not be construed as limiting.Furthermore, other forms of initial state information can be identifiedthrough possible indicators and associated effects. Examples of possibleindicators and associated effects are set forth below in Table 5.

TABLE 5 (_indicator volatility-in-the-price-of-oil a-warmer-than-normal-winter-in-the-northern-hemisphere ) (_indicatorvolatility-in-the-price-of-oil a-colder-than-normal-winter-in-the-normal-hemisphere ) (_indicatorvolatility-in-the-price-of-oil economic-decline ) (_indicatorvolatility-in-the-price-of-oil increased-demand-for-oil)

It is noted that the examples presented in Table 5 are expressed in aparticular form of artificial intelligence description language, aPlanning Domain Description Language. Other forms of artificialintelligence description languages can also be applicable, and thereforethe particular form of Table 5 should not be construed as limiting.

Hence, several methodologies for identifying an initial state from agraphical representation have been described. As shown in Table 4 andTable 5, identification of the initial state can include, for eachgraphical representation of domain knowledge being processed, updatingan initial state with the special predicate “_is-true” and a factorterm. As further shown in Table 4 and Table 5, identification of theinitial state can include, for each graphical representation of domainknowledge being processed, associating a leading indicator with possibleindicators and associated effects such as an “_indicator” and factorterm and occurrence term. Other methodologies for identifying andupdating an initial state for an artificial intelligence planningproblem can also be applicable. Therefore, these particular examplesshould not be construed as limiting.

In addition to the methodologies described above, it is noted that oneor more new, different, or changed graphical representations of domainknowledge can be received by one or more components during processing.For example, real-world changes or changes to domain knowledge can beexpressed in a new, second, or altered graphical representation ofdomain knowledge. Therefore, according to at least one embodiment, thecomputer-implemented method 400 can also include propagating the changedgraphical representation of domain knowledge. Additionally, thecomputer-implemented method 400 can include receiving a second graphicalrepresentation and updating the initial state of the domain knowledgebased on the second graphical representation. Therefore, in anembodiment where the graphical representation is changed, on-the-flychanges to domain knowledge can be handled without requiring specializedknowledge of the artificial intelligence description language.

Furthermore, the updating and/or propagating can include repeating, asdesired, any of the above-described sequences of operations such thatthe changed graphical representation of domain knowledge is translatedas described above. Thus, the computer-implemented method 400 canfurther comprise encoding changes to the initial state, changes to theone or more constants, changes to the one or more predicates, andchanges to the one or more actions into the artificial intelligencedescription language. Therefore, in an embodiment where the graphicalrepresentation is changed, any changes to domain knowledge can beautomatically propagated and encoded in the artificial intelligencedescription language without necessitating use of the artificialintelligence description language by an expert.

As described above, several translation algorithms can be applicable toembodiments, and can be performed by the translation component 204and/or the parsing component 206. The translation algorithms can alsosupport new, updated, or changed graphical representation of domainknowledge. Furthermore, the results of the translation algorithms, whichhave been described with reference to an artificial intelligenceplanning problem, can be validated according to a validation algorithmas described below.

FIG. 5 illustrates a flowchart of an example, non-limitingcomputer-implemented method 500 of translating graphical representationsof domain knowledge associated with an artificial intelligence planningproblem in accordance with one or more embodiments of the disclosedsubject matter.

The computer-implemented method 500 can comprise, for example, asequence of program instructions to be performed by a validationcomponent and/or an artificial intelligence planning component, such asthe validation component 210 and the artificial intelligence planningcomponent 212. The computer-implemented method 500 can include, but isnot limited to, block 502 and block 504. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity.

The computer-implemented method 500 can comprise determining, by thedevice, through an artificial intelligence planning component anartificial intelligence plan as a solution to the artificialintelligence planning problem, at block 502 (e.g., by artificialintelligence planning component 212). As described in detail above withreference to Definition 1 and Definition 2, several differentmethodologies of determining a solution to the artificial intelligenceplanning problem can be applicable.

As further shown in FIG. 5, the computer-implemented method 500 can alsocomprise determining, by the device, that the artificial intelligenceplan comprises a valid pathway of the graphical representation of thedomain knowledge, at block 504 (e.g., by the validation component 210).As described in detail above, the determination of a pathway of thegraphical representation of the domain knowledge can be facilitatedthrough input of the graphical representation to the validationcomponent 210. Responsive to receiving as input the graphicalrepresentation of the domain knowledge, the validation component 210 canbe configured to determine if the solution is a valid solution.Therefore, in an embodiment comprising block 502 and/or block 504, thecomputer-implemented method 500 can ensure the artificial intelligenceplanning problem is a valid artificial intelligence planning problem asrelated to the graphical representation provided by the domain expert,without requiring specialized knowledge of the artificial intelligencedescription language.

As described above, translation of graphical representations of domainknowledge into an artificial intelligence planning problem has beenprovided through the various embodiments described herein. Embodimentsdescribed in this disclosure can include, for example, acomputer-implemented method and a translation component that can providethe described solution. Embodiments described in this disclosure canalso include, for example, a computer-implemented method and avalidation component that can provide the described solution. Thecomputer-implemented method can comprise receiving a graphicalrepresentation of domain knowledge and translating the graphicalrepresentation into an artificial intelligence planning problemexpressed in an artificial intelligence description language.

One or more embodiments can be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product can include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to one or moreembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In connection with FIG. 10, the systems and processes described belowcan be embodied within hardware, such as a single integrated circuit(IC) chip, multiple ICs, an application specific integrated circuit(ASIC), or the like. Further, the order in which some or all of theprocess blocks appear in each process should not be deemed limiting.Rather, it should be understood that some of the process blocks can beexecuted in a variety of orders, not all of which can be explicitlyillustrated herein.

With reference to FIG. 10, an example environment 1000 for implementingvarious aspects of the claimed subject matter includes a computer 1002.The computer 1002 includes a processing unit 1004, a system memory 1006,a codec 1035, and a system bus 1008. The system bus 1008 couples systemcomponents including, but not limited to, the system memory 1006 to theprocessing unit 1004. The processing unit 1004 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as the processing unit 1004.

The system bus 1008 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 1006 includes volatile memory 1010 and non-volatilememory 1012, which can employ one or more of the disclosed memoryarchitectures, in various embodiments. The basic input/output system(BIOS), containing the basic routines to transfer information betweenelements within the computer 1002, such as during start-up, is stored innon-volatile memory 1012. In addition, according to present innovations,codec 1035 can include at least one of an encoder or decoder, whereinthe at least one of an encoder or decoder can consist of hardware,software, or a combination of hardware and software. Although, codec1035 is depicted as a separate component, codec 1035 can be containedwithin non-volatile memory 1012. By way of illustration, and notlimitation, non-volatile memory 1012 can include read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), Flash memory, 3D Flashmemory, or resistive memory such as resistive random access memory(RRAM). Non-volatile memory 1012 can employ one or more of the disclosedmemory devices, in at least some embodiments. Moreover, non-volatilememory 1012 can be computer memory (e.g., physically integrated withcomputer 1002 or a mainboard thereof), or removable memory. Examples ofsuitable removable memory with which disclosed embodiments can beimplemented can include a secure digital (SD) card, a compact Flash (CF)card, a universal serial bus (USB) memory stick, or the like. Volatilememory 1010 includes random access memory (RAM), which acts as externalcache memory, and can also employ one or more disclosed memory devicesin various embodiments. By way of illustration and not limitation, RAMis available in many forms such as static RAM (SRAM), dynamic RAM(DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM),and enhanced SDRAM (ESDRAM) and so forth.

Computer 1002 can also include removable/non-removable,volatile/non-volatile computer storage medium. FIG. 10 illustrates, forexample, disk storage 1014. Disk storage 1014 includes, but is notlimited to, devices like a magnetic disk drive, solid state disk (SSD),flash memory card, or memory stick. In addition, disk storage 1014 caninclude storage medium separately or in combination with other storagemedium including, but not limited to, an optical disk drive such as acompact disk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage devices 1014 tothe system bus 1008, a removable or non-removable interface is typicallyused, such as interface 1016. It is appreciated that storage devices1014 can store information related to a user. Such information might bestored at or provided to a server or to an application running on a userdevice. In one embodiment, the user can be notified (e.g., by way ofoutput device(s) 1036) of the types of information that are stored todisk storage 1014 or transmitted to the server or application. The usercan be provided the opportunity to opt-in or opt-out of having suchinformation collected or shared with the server or application (e.g., byway of input from input device(s) 1028).

It is to be appreciated that FIG. 10 describes software that acts as anintermediary between users and the basic computer resources described inthe suitable operating environment 1000. Such software includes anoperating system 1018. Operating system 1018, which can be stored ondisk storage 1014, acts to control and allocate resources of thecomputer system 1002. Applications 1020 take advantage of the managementof resources by operating system 1018 through program modules 1024, andprogram data 1026, such as the boot/shutdown transaction table and thelike, stored either in system memory 1006 or on disk storage 1014. It isto be appreciated that the claimed subject matter can be implementedwith various operating systems or combinations of operating systems.

A user enters commands or information into the computer 1002 throughinput device(s) 1028. Input devices 1028 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1004through the system bus 1008 via interface port(s) 1030. Interfaceport(s) 1030 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1036 usesome of the same type of ports as input device(s) 1028. Thus, forexample, a USB port can be used to provide input to computer 1002 and tooutput information from computer 1002 to an output device 1036. Outputadapter 1034 is provided to illustrate that there are some outputdevices 1036 like monitors, speakers, and printers, among other outputdevices 1036, which require special adapters. The output adapters 1034include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1036and the system bus 1008. It should be noted that other devices orsystems of devices provide both input and output capabilities such asremote computer(s) 1038.

Computer 1002 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1038. The remote computer(s) 1038 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device, a smart phone, a tablet, or other network node, andtypically includes many of the elements described relative to computer1002. For purposes of brevity, only a memory storage device 1040 isillustrated with remote computer(s) 1038. Remote computer(s) 1038 islogically connected to computer 1002 through a network interface 1042and then connected via communication connection(s) 1044. Networkinterface 1042 encompasses wire or wireless communication networks suchas local-area networks (LAN) and wide-area networks (WAN) and cellularnetworks. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1044 refers to the hardware/softwareemployed to connect the network interface 1042 to the bus 1008. Whilecommunication connection 1044 is shown for illustrative clarity insidecomputer 1002, it can also be external to computer 1002. Thehardware/software necessary for connection to the network interface 1042includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and wired and wirelessEthernet cards, hubs, and routers.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration and are intended to be non-limiting. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as an“example” and/or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim. The descriptions of the various embodiments have been presentedfor purposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

1. A computer-implemented method, comprising: translating, by a deviceoperatively coupled to a processor, into an artificial intelligenceplanning problem, a graphical representation comprising informationindicative of a central concept and at least one chain of eventsassociated with the central concept, wherein the translating comprisesassociating one or more weights with one or more predicates and one ormore actions, and wherein the graphical representation further comprisesone or more probabilities of an event of the at least one chain ofevents.
 2. The computer-implemented method of claim 1, wherein thetranslating further comprises: identifying one or more constants basedon a grouping of terms indicative of the graphical representation. 3.The computer-implemented method of claim 2, wherein the translatingfurther comprises: identifying the one or more predicates and the one ormore actions based on the one or more constants.
 4. Thecomputer-implemented method of claim 3, further comprising: encoding theone or more constants and the one or more actions into an artificialintelligence description language.
 5. The computer-implemented method ofclaim 1, wherein the graphical representation further comprises the oneor more weights associated with an event of the at least one chain ofevents.
 6. The computer-implemented method of claim 5, wherein theartificial intelligence planning problem comprises one or moreobservations from the graphical representation.
 7. Thecomputer-implemented method of claim 6, wherein the one or moreobservations are one or more unreliable observations.
 8. Thecomputer-implemented method of claim 1, wherein the artificialintelligence planning problem projects a future state of domainknowledge.
 9. The computer-implemented method of claim 1, wherein anaction of the one or more predicates is associated with an action costthat indicates a cost of performing the action.
 10. Thecomputer-implemented method of claim 1, wherein the one or more weightsis associated with a value indicative of a level of importance.
 11. Anon-transitory computer program product facilitating translating agraphical representation of domain knowledge, the computer programproduct comprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processing unit to cause the processing unit to: translate into anartificial intelligence planning problem, a graphical representationcomprising information indicative of a central concept and at least onechain of events associated with the central concept, wherein thetranslating comprises associating one or more weights with one or morepredicates and one or more actions, and wherein the graphicalrepresentation further comprises one or more probabilities of an eventof the at least one chain of events.
 12. The non-transitory computerprogram product of claim 11, wherein the translation further comprises:identification of one or more constants based on a grouping of termsindicative of the graphical representation.
 13. The non-transitorycomputer program product of claim 12, wherein the translation furthercomprises: identification of the one or more predicates and the one ormore actions based on the one or more constants.
 14. The non-transitorycomputer program product of claim 13, wherein the program instructionsare further executable by the processing unit to cause the processingunit to: encode the one or more constants and the one or more actionsinto an artificial intelligence description language, wherein thegraphical representation further comprises the one or more weightsassociated with an event of the at least one chain of events, andwherein the artificial intelligence planning problem comprises one ormore observations from the graphical representation.
 15. A system,comprising: a memory that stores computer executable components; and aprocessor that executes the computer executable components stored in thememory, wherein the computer executable components comprise: atranslation component that: translates into an artificial intelligenceplanning problem, a graphical representation comprising informationindicative of a central concept and at least one chain of eventsassociated with the central concept, wherein the translating comprisesassociating one or more weights with one or more predicates and one ormore actions, and wherein the graphical representation further comprisesone or more probabilities of an event of the at least one chain ofevents.
 16. The system of claim 15, wherein the translation componentalso identifies one or more constants based on a grouping of termsindicative of the graphical representation.
 17. The system of claim 16,wherein the translation component also identifies the one or morepredicates and the one or more actions based on the one or moreconstants.
 18. The system of claim 15, wherein the computer-executablecomponents also encode the one or more constants and the one or moreactions into an artificial intelligence description language.
 19. Thesystem of claim 15, wherein the graphical representation furthercomprises the one or more weights associated with an event of the atleast one chain of events.
 20. The system of claim 19, wherein theartificial intelligence planning problem comprises one or moreobservations from the graphical representation.